Differentiating Streaming, Stream Processing, and Streaming Analytics

The terms Streaming, Stream Processing, and Streaming Analytics are distinct components that together form the architecture of a real-time data platform. However, the lines between these terms often get blurred as their definitions have evolved alongside advancing technologies. The result is a marketplace where distinguishing between these terms is not only difficult but can lead to misinformed decisions. This article seeks to clarify the distinctions between these three concepts and explore how they fit into the modern real-time data ecosystem.

What is streaming?

Streaming refers to the continuous flow of data generated by various sources, such as applications, sensors, or user activity, which is immediately sent to a destination system for consumption. The key defining characteristic of streaming is its publish-subscribe nature, where producers (publishers) generate raw data events, and consumers (subscribers) receive these events in real-time, with no alteration or transformation occurring during the transmission. This raw event delivery allows multiple downstream systems to subscribe to the same stream of data simultaneously, enabling each to act on or store the data for their specific purposes without the overhead of processing it during transmission—bringing significant efficiencies in resource use and reducing data duplication efforts.

Figure 1: Streaming – The continuous flow of raw data from various sources to multiple destinations in real time without modification.

Popular open-source projects in this space include Apache Kafka, Apache Pulsar, and NATS, which provide scalable, fault-tolerant platforms for streaming data. Kafka, originally developed by LinkedIn, is particularly well known for handling high-throughput data streams, making it a favorite among large enterprises. Pulsar, originally from Yahoo!, offers more flexibility with multi-tenancy and geo-replication features. NATS is popular in the IoT space due to its simplicity and low latency.

Key vendors offering managed streaming services include Confluent (based on Kafka), Amazon Kinesis, Google Pub/Sub, and Microsoft Azure Event Hubs, all built to handle real-time event streaming at cloud-scale. Managed services provide added value by handling the complexities of infrastructure management, scaling, monitoring, and upgrades, freeing teams from the operational overhead required with pure open-source solutions, while ensuring reliability and performance at enterprise scale.

In addition to open-source options, popular proprietary tools for real-time streaming include Redpanda and Kinesis. Redpanda has gained attention for being a Kafka-compatible solution designed to simplify operations while offering lower latencies and better performance. Amazon Kinesis is a fully managed, highly scalable service that integrates seamlessly within the AWS ecosystem, making it a strong choice for businesses already leveraging AWS services. These tools offer built-in features like scaling and monitoring, providing a streamlined way for organizations to implement real-time data processing without the operational complexity of self-managed systems.

What is stream processing?

Stream processing refers to the real-time processing of data in motion. Unlike simple streaming, which just transmits raw data, stream processing involves transforming, aggregating, filtering, or enriching data on the fly before it reaches its destination. By applying real-time transformations, stream processing ensures that the data sent to downstream applications is more ready to be acted upon. 

The defining characteristic of stream processing is its ability to handle continuous, unbounded flows of data and apply transformations in real time. There are a wide range of real-time data transformation activities that make raw data immediately useful. Examples include converting raw sensor data into standardized formats, splitting sentences into individual words for further analysis, and filtering out events where the transaction amount is below a certain threshold. Additionally, stream processing allows grouping events by user ID to perform subsequent actions per user, calculating running totals for each customer’s transactions, summing total sales in a defined time window, and computing the average temperature from sensor readings over time. 

Further, and similar to an important value proposition of streaming, these transformations can be reused across multiple downstream environments in what is increasingly referred to as “shift left.” By processing and refining data in real-time before it reaches downstream systems, this approach reduces duplication of effort, enhances consistency, and ensures that multiple teams and applications can leverage the same transformed data without needing to perform redundant processing later in the workflow.

Figure 2: Stream Processing – The real-time transformation, filtering, or aggregation of streaming data as it is ingested to support multiple downstream systems.

Confusion often arises because stream processing is frequently associated with “analytics,” and while some stream processing tasks—like summing up sales every five minutes—could be considered analytical, there are important limitations to the sophistication of the analytics that can be performed by stream processing tools. These constraints typically stem from the need for low-latency, near-instantaneous processing, which limits the complexity of calculations and the volume of data that can be processed at once. Additionally, the state that needs to be maintained in-memory for real-time operations can quickly become unwieldy, further limiting the depth and scope of analytical capabilities in a streaming context. More sophisticated and scalable analytics in real-time, and how they overcome these constraints, will be explored in the next section.

Popular open-source stream processing frameworks include Apache Flink, Apache Storm, and Apache Samza, each with different strengths depending on the use case. Flink excels in stateful stream processing and exactly-once semantics, making it suitable for complex, fault-tolerant pipelines. Storm is known for low-latency processing, while Samza integrates deeply with Kafka for message-driven systems.

Managed stream processing services such as AWS Lambda (via Kinesis Data Analytics), Google Dataflow, and Azure Stream Analytics offer businesses a cloud-based solution that abstracts away the operational complexity of maintaining infrastructure. These services allow companies to focus on developing stream processing logic without worrying about scaling, fault tolerance, or managing resources, bringing efficiency and faster time to value for real-time processing workloads.

What is streaming analytics?

Streaming analytics goes beyond stream processing by enabling real-time, advanced analytical computations on continuously flowing data. While stream processing focuses on simple transformations and filtering, streaming analytics involves more complex operations such as real-time trend analysis, anomaly detection, and high cardinality aggregations. This allows businesses to extract deeper insights from data as it’s being ingested, providing immediate, actionable information that can be leveraged in the moment.

A key advantage of streaming analytics is its ability to deliver insights and trigger actions in real time. For example, a financial institution can more accurately detect fraudulent transactions by analyzing patterns in transaction streams within the context of historical data, a video game company can produce leaderboards with hundreds of dimensions, or a retailer can provide dynamic, personalized offers by analyzing customer behavior in real time. These advanced analytics empower organizations to respond instantly for insights from data as it flows, leading to faster, data-driven decisions and more responsive customer experiences.

Figure 3: Streaming Analytics – The application of real-time analysis and insights to streaming data, enabling immediate actions and decisions.

Open-source technologies like Apache Pinot, ClickHouse, and Apache Druid are foundational in the world of streaming analytics. Managed services, such as StarTree for Pinot, Altinity, ClickHouse Inc, and Tinybird for ClickHouse, and Imply for Druid, offer enterprise-ready solutions that simplify the complexities of deploying and managing these systems, as well as providing additional enterprise features such as easier data ingestion, performance optimizations, and security.

Streaming analytics: Endpoints and platforms

Confusion often arises when streaming analytics is thought of as just another downstream analytic endpoint, like a data warehouse or data lake (see figure 2), albeit with faster ingest and query speeds. Streaming analytics is increasingly seen as a core part of the streaming platform rather than just another downstream system (or endpoint). It can operate as the “last mile” of a streaming platform, where real-time insights are delivered in sync with continuous data flows, rather than as an after-the-fact analysis layer. This positions real-time analytics as integral to the real-time data platform, not merely a faster version of traditional analytics endpoints.

The main difference between real-time analytics as an endpoint and as a platform is in their reach and adaptability. An endpoint is built for a specific use, delivering tailored analytics for that purpose. Conversely, a real-time analytics platform is a central system that supports multiple applications from a single source. This platform model allows various needs to be met simultaneously, aligning with streaming and stream processing principles, which are foundational for creating flexible, organization-wide data flows and analytics capabilities.

Endpoint example: A company might set up a real-time analytics endpoint specifically to analyze clickstream data from its website to track user engagement for its marketing team. This focused solution could help the team understand which pages users visit most, how long they stay, and the paths they take, providing insights to optimize website content and campaigns.

Platform model example: A tech company could implement a real-time analytics platform that supports multiple use cases. Internally, it could power dashboards for product teams to monitor feature usage, inform operations with system health metrics, and provide insights for finance teams tracking revenue in real time. Externally, the platform might deliver real-time analytics as part of customer-facing products, offering users personalized data insights, live notifications, or instant analytics reports. This centralized system enables a wide range of data-driven functionalities across the company.

If an enterprise only requires one or two real-time analytics endpoints for internal teams, investing in a full platform approach can be excessive and unnecessary. The focused, point solution will meet their needs more efficiently. However, if an organization has numerous real-time analytics use cases, including both internal applications and customer-facing features, adopting a platform approach can offer significant cost efficiencies. A centralized platform reduces redundancy, streamlines infrastructure, and supports scalability, making it a smarter investment as the complexity and number of use cases grows.

For streaming analytics to function as a platform rather than a point solution in an enterprise setting, it must scale to handle tens or even hundreds of thousands of queries per second to support multiple applications simultaneously. Pinot, Clickhouse, and Druid start to diverge in this regard given the design principles influenced by their distinct origins. Apache Pinot was developed at LinkedIn (the same company behind Apache Kafka) to solve the challenge of delivering real-time analytics for large-scale, user-facing applications like “Who Viewed My Profile,” where millions of users demand instant insights. Pinot’s creators designed it to handle high-concurrency queries with ultra-low latency, making it ideal for external user-facing applications at massive scale. Companies like Stripe, Etsy and Uber have leveraged Apache Pinot in this way, building numerous customer-facing, real-time analytic services to support their business operations and deliver instant insights at massive scale while realizing resulting efficiencies from the centralized service platform model.

Apache Druid and ClickHouse were both originally built for ad analytics, where an internal team of buyers and sellers needed real-time insights on clickstream data to track and optimize ad performance in near real-time. While Druid and ClickHouse serve specific analytics needs in ad-tech and adjacent environments, Pinot stands out for its versatility and ability to power large-scale, customer-facing applications that require real-time insights along with high query per second (QPS) requirements.

Figure 4: Streaming Analytics is a part of a streaming platform when it is capable of supporting many use cases in a single instance.

The Kafka-Flink-Pinot (KFP) stack

Of note, an emerging stack for Streaming, Stream Processing, and Streaming Analytics is being built on the integrated open-source components of Apache Kafka, Apache Flink, and Apache Pinot—commonly referred to as the KFP Stack. This powerful combination provides a comprehensive solution for real-time data architectures. Kafka handles high-throughput streaming with its robust publish-subscribe messaging system, Flink processes the data in motion with advanced stateful stream processing capabilities, and Pinot delivers low-latency, real-time analytics at scale. Together, the KFP Stack forms a seamless platform that allows organizations to ingest, process, and analyze massive volumes of data in real time, supporting a wide range of use cases from fraud detection to personalized customer experiences.

Uber’s KFP stack supports its real-time advertising capabilities on UberEats. This platform stack processes ad events to handle auctions, bids, and performance reporting, ensuring exactly-once event processing and reliable analytics. Kafka manages message queues with scalability, Flink provides stream processing with exactly-once guarantees, and Pinot delivers low-latency OLAP analytics. This architecture enables Uber to maintain data freshness, reliability, and accuracy at scale, supporting internal user and external customer demands.

As businesses demand faster insights, the distinction between Streaming, Stream Processing, and Streaming Analytics becomes crucial. Streaming analytics is no longer just an endpoint; it’s the “last mile” of real-time data architectures that supports many applications endpoints. Misunderstanding these components can limit a company’s ability to fully capitalize on real-time data, while a clear understanding empowers businesses to deliver faster insights and greater operational efficiency.